Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	127,326
2	South Dakota	121,380
3	Rhode Island	106,097
4	Utah	105,639
5	Tennessee	102,438
6	Arizona	100,926
7	Wisconsin	100,308
8	Iowa	99,839
9	Nebraska	97,338
10	Arkansas	95,164
11	Oklahoma	95,129
12	Kansas	93,468
13	Indiana	91,802
14	Alabama	90,942
15	Idaho	89,939
16	Mississippi	89,578
17	Nevada	88,613
18	Wyoming	88,382
19	Illinois	87,677
20	Montana	86,312
21	Louisiana	83,583
22	South Carolina	82,285
23	California	81,850
24	New Mexico	81,215
25	Minnesota	80,955
26	Georgia	80,573
27	Kentucky	79,278
28	Missouri	79,110
29	Texas	79,011
30	Florida	77,650
31	Delaware	77,523
32	New Jersey	75,626
33	Ohio	74,677
34	Massachusetts	73,439
35	Alaska	72,644
36	New York	69,856
37	North Carolina	69,670
38	Connecticut	68,689
39	Colorado	67,838
40	West Virginia	65,272
41	Pennsylvania	63,922
42	Michigan	60,164
43	Maryland	57,038
44	Virginia	56,625
45	District of Columbia	50,584
46	New Hampshire	46,459
47	Washington	40,431
48	Puerto Rico	39,353
49	Oregon	33,040
50	Maine	28,052
51	Vermont	18,085
52	Hawaii	17,849

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	837
2	Arizona	779
3	Connecticut	662
4	South Carolina	639
5	New York	606
6	Louisiana	595
7	California	591
8	Virginia	572
9	Texas	561
10	Georgia	547
11	Delaware	539
12	New Jersey	532
13	Oklahoma	528
14	North Carolina	490
15	Massachusetts	487
16	Arkansas	463
17	Kentucky	458
18	Alabama	439
19	Florida	432
20	West Virginia	414
21	Mississippi	400
22	Utah	393
23	New Hampshire	391
24	Ohio	372
25	Tennessee	367
26	Pennsylvania	339
27	Wyoming	327
28	Nevada	326
29	Indiana	318
30	Kansas	308
31	Maryland	295
32	District of Columbia	294
33	Missouri	290
34	Maine	275
35	New Mexico	272
36	Illinois	262
37	Iowa	251
38	Montana	249
39	Idaho	246
40	Colorado	244
41	Wisconsin	231
42	Puerto Rico	208
43	Vermont	201
44	Nebraska	199
45	Michigan	184
46	Washington	165
47	Minnesota	158
48	South Dakota	157
49	Alaska	147
50	Oregon	138
51	North Dakota	125
52	Hawaii	75

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,376
2	New York	2,173
3	Massachusetts	2,063
4	Rhode Island	2,006
5	Mississippi	1,966
6	Connecticut	1,944
7	South Dakota	1,927
8	North Dakota	1,889
9	Louisiana	1,854
10	Arizona	1,715
11	Illinois	1,645
12	Pennsylvania	1,631
13	Arkansas	1,554
14	Michigan	1,531
15	New Mexico	1,513
16	Indiana	1,456
17	Iowa	1,423
18	Alabama	1,406
19	Tennessee	1,329
20	Nevada	1,328
21	South Carolina	1,277
22	District of Columbia	1,258
23	Kansas	1,243
24	Georgia	1,230
25	Texas	1,225
26	Florida	1,195
27	Missouri	1,153
28	Maryland	1,151
29	Montana	1,109
30	Minnesota	1,094
31	Delaware	1,093
32	Wisconsin	1,076
33	West Virginia	1,075
34	Wyoming	1,029
35	Nebraska	1,026
36	Colorado	975
37	California	967
38	Idaho	946
39	Ohio	928
40	Kentucky	843
41	North Carolina	842
42	Oklahoma	839
43	New Hampshire	731
44	Virginia	723
45	Puerto Rico	558
46	Washington	557
47	Utah	503
48	Oregon	452
49	Maine	415
50	Alaska	343
51	Hawaii	281
52	Vermont	274

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	16
2	Tennessee	16
3	Hawaii	15
4	Arizona	14
5	Arkansas	14
6	Wyoming	14
7	Montana	13
8	Rhode Island	13
9	California	11
10	Mississippi	11
11	Connecticut	10
12	West Virginia	10
13	Delaware	9
14	Louisiana	9
15	New York	9
16	Pennsylvania	9
17	District of Columbia	8
18	Kentucky	8
19	Nevada	8
20	New Mexico	8
21	Florida	7
22	Georgia	7
23	Massachusetts	7
24	Missouri	7
25	Oklahoma	7
26	Texas	7
27	Maryland	6
28	New Jersey	6
29	North Carolina	6
30	South Carolina	6
31	Illinois	5
32	Indiana	5
33	Nebraska	5
34	Ohio	5
35	Michigan	4
36	Wisconsin	4
37	Colorado	3
38	Idaho	3
39	Maine	3
40	New Hampshire	3
41	South Dakota	3
42	Utah	3
43	Virginia	3
44	Kansas	2
45	Minnesota	2
46	Oregon	2
47	Washington	2
48	North Dakota	1
49	Puerto Rico	1
50	Alaska	0
51	Iowa	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	291,371	1	99
Dewey	South Dakota	234,895	2	99
Chattahoochee	Georgia	234,162	3	99
Lincoln	Arkansas	231,726	4	99
Bent	Colorado	230,411	5	99
Davidson	Tennessee	119,670	243	92
Richland	South Carolina	82,574	1410	55
York	South Carolina	75,753	1766	43
Orange	California	75,314	1782	43
Pierce	Washington	37,737	2890	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,452	4	99
Grant	Nebraska	6,421	5	99
Davidson	Tennessee	1,031	1869	40
Richland	South Carolina	955	1991	36
Orange	California	872	2108	32
York	South Carolina	758	2276	27
Pierce	Washington	497	2647	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.